 So I'm not sure if you guys are aware, but we're from a company called chain Alice's and today we're going to dive into Building trust in the Ethereum blockchain and sort of what our company does in the space Sorry about that. I'll keep us closer Okay, cool. So just a brief agenda about what's going on So first we're going to talk about chain Alice's and our services Then we're going to dive into some of the data challenges we have Then we're actually going to have a long section analyzing some Ethereum data and walking through one of the investigations we did and then We're going to talk generally about the drive insights we gain So just a quick background about myself I'm the engineering manager for the blockchain intelligence team at chain Alice's But before this I was very interested academically in Bitcoin So for my undergraduate thesis I created a set of tools to visualize the Bitcoin blockchain This was for Imperial College and these are some of the pictures from my thesis in the top left. You can see here These are different views of Bitcoin transactions over here This one has one input and two outputs the old way of change it addresses This one has a lot of inputs and one output So that would be someone aggregating a lot of funds and then this has the reverse which is one input and many outputs So someone's disaggregating funds This has generally changed since this this thesis was completed in like 2015 So the block size was not as big of an issue back then batching was not something that was enforced or Regularly used so the blockchain itself has changed a lot since then Just a bit about myself. I really like network theory and graph analysis This fun little visualization in the corner is An algorithm I modified which some of you might know is called force Atlas to and I created multi force multi gravity force Atlas to To separate different graph entities And down here you can see a cluster over time So what does chain analysis do and what are the services we provide? Well, really we're trying to build trust in the conventional ecosystem. So I know some people here might Want a disjoint financial ecosystem, but our work is about integrating crypto companies into the existing ecosystem So we work with three major parts of that ecosystem. We work with the cryptocurrency businesses themselves Financial institutions as well as law enforcement and regulators So we found that In general the space doesn't grow because every each party doesn't feel comfortable with what's being provided So you can see over regulation occurred and some in Japan as well as Korea Related to some of the exchange hacks that happened and it caused a lot of privacy coins to actually be delisted So first we can see coin check had to delist a couple of their privacy coins Then this spread to South Korea and they started having stricter regulations And more exchanges followed suit So our goal is to provide tools that make regulators feel comfortable with these currencies and what they can do with them to some degree So overall illicit activity prevents the growth for all ecosystem participants I'm sure some of you have mixed opinions about the top half, but I'm sure we can all agree that the bottom two are bad In general each of these each of the actors in the ecosystem has their own risks and needs And we try to address each of these so for cryptocurrency based businesses. They struggle to get bank accounts It's hard to get banking relationships without having some sort of information on where your funds come from and what risk is associated with them for financial institutions if they Expose themselves too much to risk they harm their reputations and if they're large financial institutions they will face legal repercussions and Lastly for law enforcement regulators if they can't enforce the laws they make or the regulations they do to control an industry They won't be comfortable with that technology growing so overall our goal is to Work with each of the participants of this ecosystem and give them value that lets them feel more comfortable with Cryptocurrencies in the existing financial system So some of the data challenges we face We were a very Bitcoin-centric company before But now we've turned a lot to Ethereum. So here is actually Bitcoin on the left and Ethereum on the right They might look similar to you because they're graphs, but They're actually quite different in the structure underlying. So on the left. It actually captures the UTXO structure With all UTXO coins and on the right is the most more account-based structure that we see on any account-based model Additionally smart contracting provides a new layer of analysis. That's necessary We have to go beyond the transactions and go into the execution traces themselves We have to see what sort of execution happens and what information we can extract from that So this is an example of a data field in Ethereum and it's This is pretty old now because there are a lot more advanced decompilers But you basically decompile the data field and to try and figure out what the parameters that were sent are Transitioning to riskiness insights. So the tools that we provide for the participants in the ecosystem Is essentially insights on riskiness And these vary based on each of the customer themselves So if you're say in a country that doesn't consider gambling high-risk You would have customized alerts that don't say that a gambling activities high-risk for you But if you're in the US for example, that would be high-risk So we provide a customizable tool known as KYT To cryptocurrency businesses so that they can choose the right risk alerts to get All right now I'm gonna hand it off to Mikkel who will walk you through one of our investigations and analyzing Ethereum data Yeah, so my name is Mikkel and I work in our research department in Chanalysis So I actually get to do some of the fun stuff in the company So I'm gonna talk a little bit about some of the the cool stuff that we do and the the subtitle is like what happened to the Ethereum volume in 2017 so that's sort of the story that I'll be Talking about today, and then I asked my mom to send me a picture for my presentation So this is me in my early data science days. I actually think I'm playing Counter-Strike on this But just to let you know like we don't take ourselves too seriously So if you think any of this is interesting, please come talk to us afterwards. We're quite nice guys So Basically what we do is we start out with this idea of not knowing anything on the blockchain So this is one of the visualizations of addresses interacting In some blocks and then our goal is basically to turn this into this So we have an idea of what belongs together. How do the different entities interact? So basically gain knowledge about who's interacting with who on the blockchain So This is sort of the the background info. It's like we monitor the blockchain and Try to figure out what's going on and if there's something we don't understand that peaks are interest and we're like Whoa, what's going on here? And then this graph is it's the cumulative volume moved on the ethereum network and oh I can point cool So in this period here initially there was quite a normal growth and then from this period onwards It's almost looks like a piecewise function to me So you have like sort of a normal growth of volume being moved on the ethereum and then it shifts into basically a factor of five growth here So that's sort of interesting to see is like well We know a lot of things happen in 2017 a lot of ICOs There was a bull run on crypto But it doesn't really explain why you would move so much ether on the on the network So this is sort of the starting point is like we see something here or like we can't really explain it So let's figure out what's going on So we can basically if we if we divide these periods into two blocks So we're basically looking at the initial period on the network and then this high growth period and One thing we can do is we can look at the frequency distribution and what that means is basically We're taking all the transfers from that initial blue period down here and saying okay, how much volume was sent in this period and Which amount are accountable for the total volume? So this graph basically shows that more total volume was sent on the network through smaller transfer amounts So that makes sense if you're like a general economic network where it's like where most people don't have so much money So they're probably going to be interacting with smaller amounts So there's going to be more volume move through smaller amounts So this is sort of intuitive at least to me when I look at it like this looks like normal behavior So what happens when we overlay a period of that? 2017 period So this is quite interesting so this to me definitely doesn't look like normal economic behavior It almost looked like artificial is that you have these Almost smoothen out regions and then they just like sort of drop off here So when we see this one thing is quite noticeable is that more volume is being moved through larger amounts on the network So then again, you can hypothesize. Well, why are some people or entities moving more funds through larger amounts? So then you can I mean, we're basically detective So having a list of well, is it because someone wants to boost the volume artificially or are there just more people? Transacting in a normal way. This definitely doesn't look normal to me so We can also look at What type of addresses were involved in this increase in volume movement on the network? And so this graph is basically showing that for For this period like how much volume was sent through old addresses and what I mean with old addresses is addresses that were seen previously on the blockchain With respect to so if we're in this bar here for example Then old addresses means that addresses that were seen before that or if we're over here It means all all the volume that was moved by addresses that was seen before this point So it basically shows how much volume has been moved by addresses that was seen before in the blockchain And then we can also overlay a graph of how much volume is being moved by addresses Which was only or first seen in that block range. So basically newly generated addresses for any point on the timeline So when we do that that also looks kind of cool because here we definitely have a clear pattern Is that something is going on in 2017 like a lot of volume is being moved and we know it's to larger amounts And now we also know it's It's from newly generated addresses So I mean I could show you a hundred slides and this is just like a very small peak of how we would analyze something So we obviously we have a lot more information to go in and say What is like what is going on in the blockchain? But when we piece all of this together? then We can sort of We have an idea of which addresses so we know they're newly generated We know they move a lot of funds through large amounts and we know a bunch of other things also so we can sort of define which addresses are involved in this movement and then we can cluster them together and When we've done that we can sort of do graph analysis as well So we sort of look at how are they interacting are they interacting in the same way because just if you're a specific address You can still be interacting with other addresses in a different way So that doesn't really mean you should be clustered together So this is it's a long process But when we're done we basically reach something like this where this is a visualization of 25 blocks in 2017 and all the orange dots here are the addresses that were connected together. That's basically responsible for this huge movement of funds Yeah, I just like graphs, so I just wanted to give you a close-up So what can we say about? Yeah, so another thing is like we know that the address is responsible this belong together So it's one entity So what can we say about this entity when we know an entity is responsible for moving so much ETH in 2017? Well, we can go in and say okay. We know all these addresses belong together. Let's look at how much volume they send to each other and Here it's denominated as percentage of the total network volume so in these block numbers around 60 to 70 percent on average was sent inside of this entity and Then the orange is basically the funds that is going outside of this entity So this is yeah around two to three percent that's leaking over time So there's a huge difference between how much volume is being moved and how much volume is being sent outside of the entity so Yeah, that's And I would love to go into detail more about like how the entity looks and like this is a project that took several months So this is just sort of the the high level details But it's really interesting when you find something that's responsible for like for some of the periods It's over 90 percent. So over 90 percent of the volume is actually coming from one entity in 2017 on the network Yeah Back to Syria All right Well, we're a very growing company and we're expanding quite fast So in my team specifically if you want to work with Mikkel and myself We're looking for data scientists and a product manager So if either of these interest you definitely reach out to us and come chat to us in addition the company overall is Hiring across many fields And also we have major offices based around the world and New York DC Copenhagen London We have quite a few satellite offices that are smaller. I think coming up around the world So wherever you're located is probably likely you can work for us there We're also very remote friendly So even if you don't want to be based in an office and you're remote anywhere We also are very remote friendly my team in particular half of us are spread around Europe and then some of us are in New York And additionally no crypto experience is necessary. So if you want to learn about the industry if you want to get Insights we would love to train you and get you up to speed on that and you'll get it on the job And it's not necessary as a prerequisite So, yeah, if you found this talk interesting, please come speak to us in the sponsors gallery And yeah come say hi